基于分布式扩展信息滤波器的多水下机器人协同定位

G. Rui, M. Chitre
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引用次数: 15

摘要

通过利用团队成员之间的数据共享,协作式多auv定位有可能优于单auv定位。与地面通信链路不同,水下通信链路有许多与信道稳定性和带宽有关的问题,因此首选分散定位。然而,这给团队成员的合作带来了新的挑战。本文通过实例分析说明了分布式体系结构中的协作问题。为了解决这些问题,我们提出了一种基于分布式扩展信息滤波(DEIF)的多水下机器人协同定位算法。该方法只需要很小的传输数据包,并且设计用于受限的水下通信。它对丢包具有鲁棒性,在融合相关数据时提供一致的位置估计。通过仿真和现场实验数据对比,证明了该方法的有效性和优越性。
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Cooperative multi-AUV localization using distributed extended information filter
Cooperative multi-AUV localization has the potential to outperform single-AUV localization, by taking advantage of data sharing among the team members. Unlike terrestrial communication links, underwater communication links have many issues pertaining to the channel stability and bandwidth, and therefore a decentralized localization is preferred. However this opens up new challenges to team members working in cooperation. This paper uses analytical examples to illustrate the cooperation problems in the decentralized architecture. To solve these problems, we propose a new cooperative multi-AUV localization algorithm using distributed extended information filter (DEIF). The proposed method only requires small transmission packets and is designed for use under constrained underwater communication. It is robust to packet loss, providing consistent position estimates when fusing correlated data. We demonstrate the effectiveness and advantages of the proposed method with comparative results using data from simulation and field experiments.
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